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            "_view_module": "@jupyter-widgets/base",
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            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLMv2/RVL-CDIP/Fine_tuning_LayoutLMv2ForSequenceClassification_on_RVL_CDIP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zcA-eDbRsN1-"
      },
      "source": [
        "In this notebook, we are going to fine-tune `LayoutLMv2ForSequenceClassification` on the [RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/#:~:text=The%20RVL%2DCDIP%20(Ryerson%20Vision,images%2C%20and%2040%2C000%20test%20images.) dataset, which is a document image classification task. Each scanned document in the dataset belongs to one of 16 classes, such as \"resume\" or \"invoice\" (so it's a multiclass classification problem). The entire dataset consists of no less than 400,000 (!) scanned documents.\n",
        "\n",
        "For demonstration purposes, we are going to fine-tune the model on a really small subset (one example per class), and verify whether the model is able to overfit them. Note that LayoutLM achieves state-of-the-art results on RVL-CDIP, with a classification accuracy of 94.42% on the test set.\n",
        "\n",
        "* Original LayoutLMv2 paper: https://arxiv.org/abs/2012.14740"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HSpvNOQisMHF"
      },
      "source": [
        "## Setting up environment\n",
        "\n",
        "We install HuggingFace Transformers and Detectron2."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ajmsYhCrrJrf",
        "outputId": "80468eef-6ed4-49d2-fea3-2716e8f3ccc8"
      },
      "source": [
        "!pip install -q git+https://github.com/huggingface/transformers.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 50 kB 4.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 636 kB 16.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 895 kB 54.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 3.3 MB 53.2 MB/s \n",
            "\u001b[?25h  Building wheel for transformers (PEP 517) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IZ-YCl3psbhr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8ad60fbc-3d7d-4dba-d898-00ef3e5348fe"
      },
      "source": [
        "!pip install -q pyyaml==5.1\n",
        "# workaround: install old version of pytorch since detectron2 hasn't released packages for pytorch 1.9 (issue: https://github.com/facebookresearch/detectron2/issues/3158)\n",
        "!pip install -q torch==1.8.0+cu101 torchvision==0.9.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html\n",
        "\n",
        "# install detectron2 that matches pytorch 1.8\n",
        "# See https://detectron2.readthedocs.io/tutorials/install.html for instructions\n",
        "#!pip install -q detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.8/index.html\n",
        "!python -m pip install -q 'git+https://github.com/facebookresearch/detectron2.git'"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l\r\u001b[K     |█▏                              | 10 kB 33.6 MB/s eta 0:00:01\r\u001b[K     |██▍                             | 20 kB 29.6 MB/s eta 0:00:01\r\u001b[K     |███▋                            | 30 kB 18.9 MB/s eta 0:00:01\r\u001b[K     |████▉                           | 40 kB 15.5 MB/s eta 0:00:01\r\u001b[K     |██████                          | 51 kB 8.9 MB/s eta 0:00:01\r\u001b[K     |███████▏                        | 61 kB 9.2 MB/s eta 0:00:01\r\u001b[K     |████████▍                       | 71 kB 8.9 MB/s eta 0:00:01\r\u001b[K     |█████████▋                      | 81 kB 9.9 MB/s eta 0:00:01\r\u001b[K     |██████████▊                     | 92 kB 7.6 MB/s eta 0:00:01\r\u001b[K     |████████████                    | 102 kB 8.2 MB/s eta 0:00:01\r\u001b[K     |█████████████▏                  | 112 kB 8.2 MB/s eta 0:00:01\r\u001b[K     |██████████████▍                 | 122 kB 8.2 MB/s eta 0:00:01\r\u001b[K     |███████████████▌                | 133 kB 8.2 MB/s eta 0:00:01\r\u001b[K     |████████████████▊               | 143 kB 8.2 MB/s eta 0:00:01\r\u001b[K     |██████████████████              | 153 kB 8.2 MB/s eta 0:00:01\r\u001b[K     |███████████████████▏            | 163 kB 8.2 MB/s eta 0:00:01\r\u001b[K     |████████████████████▎           | 174 kB 8.2 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▌          | 184 kB 8.2 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▊         | 194 kB 8.2 MB/s eta 0:00:01\r\u001b[K     |████████████████████████        | 204 kB 8.2 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████       | 215 kB 8.2 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▎     | 225 kB 8.2 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▌    | 235 kB 8.2 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▊   | 245 kB 8.2 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▉  | 256 kB 8.2 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████ | 266 kB 8.2 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 274 kB 8.2 MB/s \n",
            "\u001b[?25h  Building wheel for pyyaml (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 763.5 MB 15 kB/s \n",
            "\u001b[K     |████████████████████████████████| 17.3 MB 290 kB/s \n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchtext 0.10.0 requires torch==1.9.0, but you have torch 1.8.0+cu101 which is incompatible.\u001b[0m\n",
            "\u001b[K     |████████████████████████████████| 49 kB 3.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 74 kB 4.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 145 kB 73.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 130 kB 74.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 745 kB 55.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 743 kB 47.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 112 kB 73.9 MB/s \n",
            "\u001b[?25h  Building wheel for detectron2 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for fvcore (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W--1--dfwFrd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0fab7891-a90a-4d98-8961-40e9206707f9"
      },
      "source": [
        "!pip install -q datasets"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 264 kB 7.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 243 kB 61.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 118 kB 69.7 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lAAP2zLitLx0"
      },
      "source": [
        "We also install PyTesseract:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "82ay-D49tLf_",
        "outputId": "fc94ad6c-a4d0-43b0-e4d4-0eb902bd54f0"
      },
      "source": [
        "! sudo apt install tesseract-ocr\n",
        "! pip install -q pytesseract"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'sudo apt autoremove' to remove it.\n",
            "The following additional packages will be installed:\n",
            "  tesseract-ocr-eng tesseract-ocr-osd\n",
            "The following NEW packages will be installed:\n",
            "  tesseract-ocr tesseract-ocr-eng tesseract-ocr-osd\n",
            "0 upgraded, 3 newly installed, 0 to remove and 40 not upgraded.\n",
            "Need to get 4,795 kB of archives.\n",
            "After this operation, 15.8 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 tesseract-ocr-eng all 4.00~git24-0e00fe6-1.2 [1,588 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu bionic/universe amd64 tesseract-ocr-osd all 4.00~git24-0e00fe6-1.2 [2,989 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu bionic/universe amd64 tesseract-ocr amd64 4.00~git2288-10f4998a-2 [218 kB]\n",
            "Fetched 4,795 kB in 2s (2,837 kB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 3.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package tesseract-ocr-eng.\n",
            "(Reading database ... 148489 files and directories currently installed.)\n",
            "Preparing to unpack .../tesseract-ocr-eng_4.00~git24-0e00fe6-1.2_all.deb ...\n",
            "Unpacking tesseract-ocr-eng (4.00~git24-0e00fe6-1.2) ...\n",
            "Selecting previously unselected package tesseract-ocr-osd.\n",
            "Preparing to unpack .../tesseract-ocr-osd_4.00~git24-0e00fe6-1.2_all.deb ...\n",
            "Unpacking tesseract-ocr-osd (4.00~git24-0e00fe6-1.2) ...\n",
            "Selecting previously unselected package tesseract-ocr.\n",
            "Preparing to unpack .../tesseract-ocr_4.00~git2288-10f4998a-2_amd64.deb ...\n",
            "Unpacking tesseract-ocr (4.00~git2288-10f4998a-2) ...\n",
            "Setting up tesseract-ocr-osd (4.00~git24-0e00fe6-1.2) ...\n",
            "Setting up tesseract-ocr-eng (4.00~git24-0e00fe6-1.2) ...\n",
            "Setting up tesseract-ocr (4.00~git2288-10f4998a-2) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "  Building wheel for pytesseract (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xkqb9IEqsp82"
      },
      "source": [
        "## Getting the data\n",
        "\n",
        "Next, we download a small subset of the RVL-CDIP dataset (which I prepared), containing 15 documents (one example per class). I omitted the \"handwritten\" class, because the OCR results were mediocre.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HKjGPvSasJIZ"
      },
      "source": [
        "import requests, zipfile, io\n",
        "\n",
        "def download_data():\n",
        "    url = \"https://www.dropbox.com/s/kuw05qmc4uy474d/RVL_CDIP_one_example_per_class.zip?dl=1\"\n",
        "    r = requests.get(url)\n",
        "    z = zipfile.ZipFile(io.BytesIO(r.content))\n",
        "    z.extractall()\n",
        "\n",
        "download_data()"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hGYTpiazsu75"
      },
      "source": [
        "Let's look at a random training example (in this case, a resume):\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OfeUA_IDsuK4",
        "outputId": "0b16afd6-cca9-4622-d6c8-d6e353ac8c4d"
      },
      "source": [
        "from PIL import Image, ImageDraw, ImageFont\n",
        "\n",
        "image = Image.open(\"/content/RVL_CDIP_one_example_per_class/resume/0000157402.tif\")\n",
        "image = image.convert(\"RGB\")\n",
        "image"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=762x1000 at 0x7F93AF24D690>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h3m130UjtGia"
      },
      "source": [
        "We can use the Tesseract OCR engine to turn the image into a list of recognized words:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171
        },
        "id": "l9nYK4YJswdC",
        "outputId": "ca8862be-fbbc-4d06-a519-d4d7261903bb"
      },
      "source": [
        "import pytesseract\n",
        "import numpy as np\n",
        "\n",
        "ocr_df = pytesseract.image_to_data(image, output_type='data.frame')\n",
        "ocr_df = ocr_df.dropna().reset_index(drop=True)\n",
        "float_cols = ocr_df.select_dtypes('float').columns\n",
        "ocr_df[float_cols] = ocr_df[float_cols].round(0).astype(int)\n",
        "ocr_df = ocr_df.replace(r'^\\s*$', np.nan, regex=True)\n",
        "words = ' '.join([word for word in ocr_df.text if str(word) != 'nan'])\n",
        "words"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"ot uv STATEMENT OF JEAN D, GIBBONS My name 4s Jean Dickinson Gibbons. My current position is Professor of Statistics and Chairman of the Applied Statistics Program at the Graduate School of the University of Alabana, I am currently a Fellow of both the American Statistical Association and the International Statistical Institute and a menber of the Committee on National Statistics of the National, Acadeny of Scdences. I received the bachelor's and master's degrees in mathematics from Duke University and the Ph.D. degree in statistics from Virginia Polytechnic Institute and State University. My previous faculty appointments were at the University of Pennsylvania and the University of Cincinnati. I was a senior Fulbright-Hays scholar at the Indian Statistical Institute in 1973. Twas Associate Editor of The Anertcan Statistician for eight years, currently act as editortal collaborator on many statistical journals, includ~ Technometrics, and serve as a reviewer for grant proposals to the National Science Foundation, I am @ member of several professional societies and have served two terms on the Board of Directors of the American Statistical Ascocdation. My publications include four scholarly books on statistics and over 30 articles in refereed professional and learned journals in my field, T was named Outstanding Scholar in 1981 and Board of Visitors Research Professor in 1974 at the University of Alabama, My current curriculua vita ig attached to this statement. stsepotzs\""
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sJddpf5ito_7"
      },
      "source": [
        "However, we can use `LayoutLMv2Processor` to easily prepare the data for the model. We give a document image as input to the processor, and it will create `input_ids`, `attention_mask`, `token_type_ids` and `bbox` for us. Internally, it will apply PyTesseract to get the words and bounding boxes, it will normalize the bounding boxes according to the size of the image, and it will turn everything into token-level inputs. It will also resize the document image to 224x224, as the model also requires an `image` input. Handy, isn't it?\n",
        "\n",
        "Btw, if you prefer to use your own OCR engine, you still can. In that case, you can provide your own words and (normalized) bounding boxes to the processor."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fhnBHY_KwvZm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "7e21391ffa3f4662aa5cc66eaf366b96",
            "4e58a378fc39403da4a2d9344b513bea",
            "b439d7495fc643a5a1beb4f88fcc996e",
            "d00358a73fe04295ba026b785a144e32",
            "30b8143dadad4911a2595205566d632c",
            "e6a2a7be91614c049a410cb5d23e35da",
            "8ea8a17c9c6f4b05a1e4ef41557bdf02",
            "3bfec86cb09e4d64b1ed27ad05d429fc",
            "1faf06a28ba342c7a57300e284bdf317",
            "082af9600b34495f8ad79051edddd28f",
            "a968d410a14b42c1b47aef141d984074",
            "5fe8762ab2ad4fa0a519f6147d1b41cd",
            "8c361d3b3cd441a0b1f0fed7491a3546",
            "ef565226d35f4503bbbffbddf4013384",
            "4d043f76043b4baba13a7d7ab4ba128c",
            "9908a3bc72c543f78fd1a5a7a84e8682",
            "db1bc8a7e0a9411fa25b26772d2d4cd7",
            "41f4ca4b12564c6194760f5bcd1d9fc2",
            "a6bd235e258b45ca90c55e4f4c5dfd44",
            "3c094820238d4717af0c98e4e59b5dad",
            "698af9712a514f24b751f28885a90216",
            "ff0e39b9da354cdfa83cae42963e1428"
          ]
        },
        "outputId": "a7481d22-8cd0-4e9b-edf4-7a98277ed02c"
      },
      "source": [
        "from transformers import LayoutLMv2FeatureExtractor, LayoutLMv2Tokenizer, LayoutLMv2Processor\n",
        "\n",
        "feature_extractor = LayoutLMv2FeatureExtractor()\n",
        "tokenizer = LayoutLMv2Tokenizer.from_pretrained(\"microsoft/layoutlmv2-base-uncased\")\n",
        "processor = LayoutLMv2Processor(feature_extractor, tokenizer)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7e21391ffa3f4662aa5cc66eaf366b96",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5fe8762ab2ad4fa0a519f6147d1b41cd",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/707 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-yLaOFB9wwcy"
      },
      "source": [
        "encoded_inputs = processor(image, return_tensors=\"pt\")"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pt1Mz59_xAGu",
        "outputId": "bb570fa9-1945-4c85-84be-7bc9c3450727"
      },
      "source": [
        "for k,v in encoded_inputs.items():\n",
        "  print(k, v.shape)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input_ids torch.Size([1, 286])\n",
            "bbox torch.Size([1, 286, 4])\n",
            "token_type_ids torch.Size([1, 286])\n",
            "attention_mask torch.Size([1, 286])\n",
            "image torch.Size([1, 3, 224, 224])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a1lmsz_UxD83"
      },
      "source": [
        "Let's check whether the `input_ids` are created correctly by decoding them back to text:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171
        },
        "id": "XgT93GFwxHvv",
        "outputId": "9711d28d-614c-4689-a842-e445d812ab50"
      },
      "source": [
        "processor.tokenizer.decode(encoded_inputs.input_ids.squeeze().tolist())"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"[CLS] ot uv statement of jean d, gibbons my name 4s jean dickinson gibbons. my current position is professor of statistics and chairman of the applied statistics program at the graduate school of the university of alabana, i am currently a fellow of both the american statistical association and the international statistical institute and a menber of the committee on national statistics of the national, acadeny of scdences. i received the bachelor's and master's degrees in mathematics from duke university and the ph. d. degree in statistics from virginia polytechnic institute and state university. my previous faculty appointments were at the university of pennsylvania and the university of cincinnati. i was a senior fulbright - hays scholar at the indian statistical institute in 1973. twas associate editor of the anertcan statistician for eight years, currently act as editortal collaborator on many statistical journals, includ ~ technometrics, and serve as a reviewer for grant proposals to the national science foundation, i am @ member of several professional societies and have served two terms on the board of directors of the american statistical ascocdation. my publications include four scholarly books on statistics and over 30 articles in refereed professional and learned journals in my field, t was named outstanding scholar in 1981 and board of visitors research professor in 1974 at the university of alabama, my current curriculua vita ig attached to this statement. stsepotzs [SEP]\""
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XKRHHvTGx6uY"
      },
      "source": [
        "Note that it also adds the special tokens ([CLS] and [SEP]). You can also pad to a max length, truncate, etc., just like you would do with a regular tokenizer."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UQzO3RLQwWjW"
      },
      "source": [
        "## Preprocessing the data using 🤗 datasets\n",
        "\n",
        "First, we convert the dataset into a Pandas dataframe, having 2 columns: image_path and label."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hs3qHEUowRyW",
        "outputId": "f3b875dc-b78a-4da4-b081-5918cbb94fd6"
      },
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "\n",
        "dataset_path = \"/content/RVL_CDIP_one_example_per_class\"\n",
        "labels = [label for label in os.listdir(dataset_path)]\n",
        "id2label = {v: k for v, k in enumerate(labels)}\n",
        "label2id = {k: v for v, k in enumerate(labels)}\n",
        "label2id"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'advertisement': 12,\n",
              " 'budget': 0,\n",
              " 'email': 8,\n",
              " 'file_folder': 10,\n",
              " 'form': 6,\n",
              " 'invoice': 11,\n",
              " 'letter': 3,\n",
              " 'memo': 7,\n",
              " 'news_article': 9,\n",
              " 'presentation': 4,\n",
              " 'questionnaire': 5,\n",
              " 'resume': 14,\n",
              " 'scientific_publication': 13,\n",
              " 'scientific_report': 1,\n",
              " 'specification': 2}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "KwDKGXo5wcbB",
        "outputId": "98b7df9d-a9c4-4244-b1be-6b365db47585"
      },
      "source": [
        "images = []\n",
        "labels = []\n",
        "\n",
        "for label_folder, _, file_names in os.walk(dataset_path):\n",
        "  if label_folder != dataset_path:\n",
        "    label = label_folder[40:]\n",
        "    for _, _, image_names in os.walk(label_folder):\n",
        "      relative_image_names = []\n",
        "      for image_file in image_names:\n",
        "        relative_image_names.append(dataset_path + \"/\" + label + \"/\" + image_file)\n",
        "      images.extend(relative_image_names)\n",
        "      labels.extend([label] * len (relative_image_names)) \n",
        "\n",
        "data = pd.DataFrame.from_dict({'image_path': images, 'label': labels})\n",
        "data.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>image_path</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>/content/RVL_CDIP_one_example_per_class/budget...</td>\n",
              "      <td>budget</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>/content/RVL_CDIP_one_example_per_class/scient...</td>\n",
              "      <td>scientific_report</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>/content/RVL_CDIP_one_example_per_class/specif...</td>\n",
              "      <td>specification</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>/content/RVL_CDIP_one_example_per_class/letter...</td>\n",
              "      <td>letter</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>/content/RVL_CDIP_one_example_per_class/presen...</td>\n",
              "      <td>presentation</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                          image_path              label\n",
              "0  /content/RVL_CDIP_one_example_per_class/budget...             budget\n",
              "1  /content/RVL_CDIP_one_example_per_class/scient...  scientific_report\n",
              "2  /content/RVL_CDIP_one_example_per_class/specif...      specification\n",
              "3  /content/RVL_CDIP_one_example_per_class/letter...             letter\n",
              "4  /content/RVL_CDIP_one_example_per_class/presen...       presentation"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ip9KymiVwgN2"
      },
      "source": [
        "from datasets import Dataset \n",
        "\n",
        "# read dataframe as HuggingFace Datasets object\n",
        "dataset = Dataset.from_pandas(data)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IaYUnqdLuXbe",
        "outputId": "f3e163b7-6ae6-4c14-a2ee-ceb2af585d29"
      },
      "source": [
        "dataset"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['image_path', 'label'],\n",
              "    num_rows: 15\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "f755a11decc4435383def55983764a24",
            "7c725f63b88a4f68bd4ee552694b09d3",
            "29e3534722bf40c29ae04d16f59f6fbf",
            "bd4db301cb4740fda929b8d6f02279ac",
            "5f839c26dece4c5ca5d68d11e296881d",
            "b10025145ecc42adac5534c5b6977f2c",
            "5f06f11ca91e41f08fc4f61c12fddaec",
            "dac15a0ba7f5425c9ca1d71641dae80a",
            "1ced2d6f21744ed28be7e3d2a13175e9",
            "080745a7d5bd47eaa02317f753ca8aa5",
            "33e848be9cae475b970f2bf683d885df"
          ]
        },
        "id": "qNS6bjXVytvF",
        "outputId": "8b26da6f-9115-4ab2-9d3f-13341c48e69d"
      },
      "source": [
        "from datasets import Features, Sequence, ClassLabel, Value, Array2D, Array3D\n",
        "\n",
        "# we need to define custom features\n",
        "features = Features({\n",
        "    'image': Array3D(dtype=\"int64\", shape=(3, 224, 224)),\n",
        "    'input_ids': Sequence(feature=Value(dtype='int64')),\n",
        "    'attention_mask': Sequence(Value(dtype='int64')),\n",
        "    'token_type_ids': Sequence(Value(dtype='int64')),\n",
        "    'bbox': Array2D(dtype=\"int64\", shape=(512, 4)),\n",
        "    'labels': ClassLabel(num_classes=len(labels), names=labels),\n",
        "})\n",
        "\n",
        "def preprocess_data(examples):\n",
        "  # take a batch of images\n",
        "  images = [Image.open(path).convert(\"RGB\") for path in examples['image_path']]\n",
        "  \n",
        "  encoded_inputs = processor(images, padding=\"max_length\", truncation=True)\n",
        "  \n",
        "  # add labels\n",
        "  encoded_inputs[\"labels\"] = [label2id[label] for label in examples[\"label\"]]\n",
        "\n",
        "  return encoded_inputs\n",
        "\n",
        "encoded_dataset = dataset.map(preprocess_data, remove_columns=dataset.column_names, features=features, \n",
        "                              batched=True, batch_size=2)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f755a11decc4435383def55983764a24",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/8 [00:00<?, ?ba/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u1j6YVse1AlC"
      },
      "source": [
        "Next, we set the format to PyTorch tensors. We also specify to put everything on the GPU (CUDA)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k_Ie3FiQ1AOF"
      },
      "source": [
        "encoded_dataset.set_format(type=\"torch\", device=\"cuda\")"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sBR9uR8k1Eou"
      },
      "source": [
        "We can create a PyTorch dataloader now:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J9UDVir9zNZ1"
      },
      "source": [
        "import torch\n",
        "\n",
        "dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=4)\n",
        "batch = next(iter(dataloader))"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BLWNOM9r1NMG",
        "outputId": "3ef12598-eb40-475b-a8f2-64c91c8ad34d"
      },
      "source": [
        "for k,v in batch.items():\n",
        "  print(k, v.shape)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "image torch.Size([4, 3, 224, 224])\n",
            "input_ids torch.Size([4, 512])\n",
            "attention_mask torch.Size([4, 512])\n",
            "token_type_ids torch.Size([4, 512])\n",
            "bbox torch.Size([4, 512, 4])\n",
            "labels torch.Size([4])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171
        },
        "id": "cYNzW2n91R1N",
        "outputId": "6297b7f0-7af9-4d6e-c06d-0eef7340f927"
      },
      "source": [
        "processor.tokenizer.decode(batch['input_ids'][0].tolist())"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'[CLS] the tobacco institute check request vendor # date : december 18, 1995 ‘ amount : $ 461. 86 pay to : datatimes p. o. box 99733 ‘ oklahoma city, ok 73199 explanation : invoice for one month of coverage. distribution of charges project code 1099 or use tax amount $ 461. 86 $ 461. 86 requested by : approyed by : laisac spe mail check to vendor : yes return check to : n / a specify date check is to be mailed : by 12 / 29 / 95 ‘ you must fill - in the requested by and approved by fields before you send this form to accounting confidential : tobacco litigation ‘ r1ok 0000337 [SEP] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]'"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "plD5KuDB1XY0",
        "outputId": "a168cfc5-2c66-4c8f-a150-bf50c86d7a3a"
      },
      "source": [
        "id2label[batch['labels'][0].item()]"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'budget'"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UETppfZA1ehC"
      },
      "source": [
        "## Define the model\n",
        "\n",
        "Here we define the model, namely `LayoutLMv2ForSequenceClassification`. We initialize it with the weights of the pre-trained base model (`LayoutLMModel`). The weights of the classification head are randomly initialized, and will be fine-tuned together with the weights of the base model on our tiny dataset. Once loaded, we move it to the GPU."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "62b1a7a01f024f09a611b0e82f8dc23e",
            "09017fe832ca4f7384920161feaa9503",
            "bda1051b80d5470b87a7869796865653",
            "09eecf3422544bd99cfdbbaaba6bdf00",
            "31b9f80e618049b89bdfa64c7d874fbf",
            "864b030d79e44ab59072a07c8f3a80d1",
            "541005dffad14d2c8bd267f9b1a557e5",
            "415a564c60ea495dbbcf9dc767e6277f",
            "0e2c43f1d3ef4678828e9f3b58ee96db",
            "27bbe66f0d57416cad8981ac23cef025",
            "b8b54fba6a884e85a9e96dad27e0e1a9"
          ]
        },
        "id": "gGiPKKhO1atK",
        "outputId": "e615750a-1744-4928-8b35-bbb5fed16d24"
      },
      "source": [
        "from transformers import LayoutLMv2ForSequenceClassification\n",
        "import torch\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "model = LayoutLMv2ForSequenceClassification.from_pretrained(\"microsoft/layoutlmv2-base-uncased\", \n",
        "                                                            num_labels=len(labels))\n",
        "model.to(device)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "62b1a7a01f024f09a611b0e82f8dc23e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/802M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at microsoft/layoutlmv2-base-uncased were not used when initializing LayoutLMv2ForSequenceClassification: ['layoutlmv2.visual.backbone.bottom_up.res3.0.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.17.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res2.2.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res2.0.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.7.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.16.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.1.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res3.1.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.18.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.13.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.3.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.9.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.8.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.1.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res2.1.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.21.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.12.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res3.1.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.6.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res2.2.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.16.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res5.1.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.0.shortcut.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res5.0.shortcut.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res5.2.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.19.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.20.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.22.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res3.3.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.5.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.10.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.21.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res2.0.shortcut.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res5.2.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res5.2.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.11.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.5.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.19.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res3.3.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.18.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res5.1.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.4.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res3.2.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.1.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.4.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res3.0.shortcut.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.9.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.6.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res3.2.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res5.0.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.stem.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.2.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res2.1.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.4.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.15.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.18.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.22.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.16.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.19.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.15.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.10.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.9.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res2.0.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res3.2.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.15.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res3.0.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res5.0.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.14.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res5.0.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.3.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.7.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.12.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res3.3.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.13.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.0.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.12.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.17.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.20.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.20.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.7.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.22.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.11.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.13.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.3.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.0.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res2.2.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res2.1.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.5.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.8.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.2.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.2.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res3.0.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.14.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res5.1.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.8.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.6.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.11.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.21.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.0.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.10.conv3.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res3.1.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.17.conv1.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res4.14.conv2.norm.num_batches_tracked', 'layoutlmv2.visual.backbone.bottom_up.res2.0.conv1.norm.num_batches_tracked']\n",
            "- This IS expected if you are initializing LayoutLMv2ForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing LayoutLMv2ForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of LayoutLMv2ForSequenceClassification were not initialized from the model checkpoint at microsoft/layoutlmv2-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LayoutLMv2ForSequenceClassification(\n",
              "  (layoutlmv2): LayoutLMv2Model(\n",
              "    (embeddings): LayoutLMv2Embeddings(\n",
              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (x_position_embeddings): Embedding(1024, 128)\n",
              "      (y_position_embeddings): Embedding(1024, 128)\n",
              "      (h_position_embeddings): Embedding(1024, 128)\n",
              "      (w_position_embeddings): Embedding(1024, 128)\n",
              "      (token_type_embeddings): Embedding(2, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (visual): LayoutLMv2VisualBackbone(\n",
              "      (backbone): FPN(\n",
              "        (fpn_lateral2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "        (fpn_output2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (fpn_lateral3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "        (fpn_output3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (fpn_lateral4): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "        (fpn_output4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (fpn_lateral5): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "        (fpn_output5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (top_block): LastLevelMaxPool()\n",
              "        (bottom_up): ResNet(\n",
              "          (stem): BasicStem(\n",
              "            (conv1): Conv2d(\n",
              "              3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False\n",
              "              (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
              "            )\n",
              "          )\n",
              "          (res2): Sequential(\n",
              "            (0): BottleneckBlock(\n",
              "              (shortcut): Conv2d(\n",
              "                64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
              "              )\n",
              "              (conv1): Conv2d(\n",
              "                64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (1): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (2): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (res3): Sequential(\n",
              "            (0): BottleneckBlock(\n",
              "              (shortcut): Conv2d(\n",
              "                256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
              "              )\n",
              "              (conv1): Conv2d(\n",
              "                256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (1): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (2): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (3): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (res4): Sequential(\n",
              "            (0): BottleneckBlock(\n",
              "              (shortcut): Conv2d(\n",
              "                512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv1): Conv2d(\n",
              "                512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                1024, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (1): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (2): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (3): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (4): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (5): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (6): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (7): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (8): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (9): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (10): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (11): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (12): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (13): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (14): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (15): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (16): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (17): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (18): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (19): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (20): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (21): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (22): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (res5): Sequential(\n",
              "            (0): BottleneckBlock(\n",
              "              (shortcut): Conv2d(\n",
              "                1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
              "              )\n",
              "              (conv1): Conv2d(\n",
              "                1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                2048, 2048, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                2048, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (1): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                2048, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                2048, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "            (2): BottleneckBlock(\n",
              "              (conv1): Conv2d(\n",
              "                2048, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
              "              )\n",
              "              (conv2): Conv2d(\n",
              "                2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
              "              )\n",
              "              (conv3): Conv2d(\n",
              "                2048, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
              "                (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (pool): AdaptiveAvgPool2d(output_size=[7, 7])\n",
              "    )\n",
              "    (visual_proj): Linear(in_features=256, out_features=768, bias=True)\n",
              "    (visual_LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "    (visual_dropout): Dropout(p=0.1, inplace=False)\n",
              "    (encoder): LayoutLMv2Encoder(\n",
              "      (layer): ModuleList(\n",
              "        (0): LayoutLMv2Layer(\n",
              "          (attention): LayoutLMv2Attention(\n",
              "            (self): LayoutLMv2SelfAttention(\n",
              "              (qkv_linear): Linear(in_features=768, out_features=2304, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMv2SelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMv2Intermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): LayoutLMv2Output(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (1): LayoutLMv2Layer(\n",
              "          (attention): LayoutLMv2Attention(\n",
              "            (self): LayoutLMv2SelfAttention(\n",
              "              (qkv_linear): Linear(in_features=768, out_features=2304, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMv2SelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMv2Intermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): LayoutLMv2Output(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (2): LayoutLMv2Layer(\n",
              "          (attention): LayoutLMv2Attention(\n",
              "            (self): LayoutLMv2SelfAttention(\n",
              "              (qkv_linear): Linear(in_features=768, out_features=2304, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMv2SelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMv2Intermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): LayoutLMv2Output(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (3): LayoutLMv2Layer(\n",
              "          (attention): LayoutLMv2Attention(\n",
              "            (self): LayoutLMv2SelfAttention(\n",
              "              (qkv_linear): Linear(in_features=768, out_features=2304, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMv2SelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMv2Intermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): LayoutLMv2Output(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (4): LayoutLMv2Layer(\n",
              "          (attention): LayoutLMv2Attention(\n",
              "            (self): LayoutLMv2SelfAttention(\n",
              "              (qkv_linear): Linear(in_features=768, out_features=2304, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMv2SelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMv2Intermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): LayoutLMv2Output(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (5): LayoutLMv2Layer(\n",
              "          (attention): LayoutLMv2Attention(\n",
              "            (self): LayoutLMv2SelfAttention(\n",
              "              (qkv_linear): Linear(in_features=768, out_features=2304, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMv2SelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMv2Intermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): LayoutLMv2Output(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (6): LayoutLMv2Layer(\n",
              "          (attention): LayoutLMv2Attention(\n",
              "            (self): LayoutLMv2SelfAttention(\n",
              "              (qkv_linear): Linear(in_features=768, out_features=2304, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMv2SelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMv2Intermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): LayoutLMv2Output(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (7): LayoutLMv2Layer(\n",
              "          (attention): LayoutLMv2Attention(\n",
              "            (self): LayoutLMv2SelfAttention(\n",
              "              (qkv_linear): Linear(in_features=768, out_features=2304, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMv2SelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMv2Intermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): LayoutLMv2Output(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (8): LayoutLMv2Layer(\n",
              "          (attention): LayoutLMv2Attention(\n",
              "            (self): LayoutLMv2SelfAttention(\n",
              "              (qkv_linear): Linear(in_features=768, out_features=2304, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMv2SelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMv2Intermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): LayoutLMv2Output(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (9): LayoutLMv2Layer(\n",
              "          (attention): LayoutLMv2Attention(\n",
              "            (self): LayoutLMv2SelfAttention(\n",
              "              (qkv_linear): Linear(in_features=768, out_features=2304, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMv2SelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMv2Intermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): LayoutLMv2Output(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (10): LayoutLMv2Layer(\n",
              "          (attention): LayoutLMv2Attention(\n",
              "            (self): LayoutLMv2SelfAttention(\n",
              "              (qkv_linear): Linear(in_features=768, out_features=2304, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMv2SelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMv2Intermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): LayoutLMv2Output(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (11): LayoutLMv2Layer(\n",
              "          (attention): LayoutLMv2Attention(\n",
              "            (self): LayoutLMv2SelfAttention(\n",
              "              (qkv_linear): Linear(in_features=768, out_features=2304, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMv2SelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMv2Intermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): LayoutLMv2Output(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (rel_pos_bias): Linear(in_features=32, out_features=12, bias=False)\n",
              "      (rel_pos_x_bias): Linear(in_features=64, out_features=12, bias=False)\n",
              "      (rel_pos_y_bias): Linear(in_features=64, out_features=12, bias=False)\n",
              "    )\n",
              "    (pooler): LayoutLMv2Pooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              "  (classifier): Linear(in_features=2304, out_features=15, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gIb5tkE-52U5"
      },
      "source": [
        "## Train the model\n",
        "\n",
        "Here we train the model in familiar PyTorch fashion. We use the Adam optimizer with weight decay fix (normally you can also specify which variables should have weight decay and which not + a learning rate scheduler, see here for how the authors of LayoutLM did this), and train for 30 epochs. If the model is able to overfit it, then it means there are no issues and we can train it on the entire dataset."
      ]
    },
    {
      "cell_type": "code",
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            "5338290a0fc84073af5d59b853a80d80",
            "105a4e9353554bdfa466242882962a38",
            "60cb6e34a749475ca6710c603dbb7b8f",
            "de14b4ed9de645c695598c2a022c0efe",
            "4fb3506a08ae4e6da8efddc37c7f1a23",
            "37697e780b1547ce80447c44b96da89c"
          ]
        },
        "id": "G3t2Awmc5wox",
        "outputId": "e1ba3780-3759-4489-c2e6-494974f2e00b"
      },
      "source": [
        "from transformers import AdamW\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "optimizer = AdamW(model.parameters(), lr=5e-5)\n",
        "\n",
        "global_step = 0\n",
        "num_train_epochs = 10\n",
        "t_total = len(dataloader) * num_train_epochs # total number of training steps \n",
        "\n",
        "#put the model in training mode\n",
        "model.train()\n",
        "for epoch in range(num_train_epochs):\n",
        "  print(\"Epoch:\", epoch)\n",
        "  running_loss = 0.0\n",
        "  correct = 0\n",
        "  for batch in tqdm(dataloader):\n",
        "      # forward pass\n",
        "      outputs = model(**batch)\n",
        "      loss = outputs.loss\n",
        "\n",
        "      running_loss += loss.item()\n",
        "      predictions = outputs.logits.argmax(-1)\n",
        "      correct += (predictions == batch['labels']).float().sum()\n",
        "\n",
        "      # backward pass to get the gradients \n",
        "      loss.backward()\n",
        "\n",
        "      # update\n",
        "      optimizer.step()\n",
        "      optimizer.zero_grad()\n",
        "      global_step += 1\n",
        "  \n",
        "  print(\"Loss:\", running_loss / batch[\"input_ids\"].shape[0])\n",
        "  accuracy = 100 * correct / len(data)\n",
        "  print(\"Training accuracy:\", accuracy.item())"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "02f8ad738cdf4e19a9474a1c44c3ff99",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/4 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss: 3.9261969725290933\n",
            "Training accuracy: 0.0\n",
            "Epoch: 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3e8874275bfe46a493114846e5ac6cea",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/4 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss: 3.4952540397644043\n",
            "Training accuracy: 20.000001907348633\n",
            "Epoch: 2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "34b15205c38a4812a80b934526b6b6b0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/4 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss: 3.200271209081014\n",
            "Training accuracy: 40.000003814697266\n",
            "Epoch: 3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f49b3b2cfea34b86b542baeea1b82396",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/4 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss: 2.867506663004557\n",
            "Training accuracy: 66.66667175292969\n",
            "Epoch: 4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e7d24a48edc543ff85081b8cbd0427d7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/4 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss: 2.77290674050649\n",
            "Training accuracy: 66.66667175292969\n",
            "Epoch: 5\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9ae33010d483422e8b92ab1a5833d59e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/4 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss: 2.384514490763346\n",
            "Training accuracy: 93.33333587646484\n",
            "Epoch: 6\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f2246caf9e6b460e915cb740045799be",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/4 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss: 1.9513905843098958\n",
            "Training accuracy: 100.00000762939453\n",
            "Epoch: 7\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "acee8902357e4f34b4427ccffff24380",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/4 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss: 1.6512147188186646\n",
            "Training accuracy: 100.00000762939453\n",
            "Epoch: 8\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "33315f350c7b4d5f88dd23703ca571f7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/4 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss: 1.3799012104670207\n",
            "Training accuracy: 100.00000762939453\n",
            "Epoch: 9\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b61413f08d614150816145aad1b2afd7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/4 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss: 1.0821181138356526\n",
            "Training accuracy: 100.00000762939453\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2dIvjZWDLbIU"
      },
      "source": [
        "## Inference\n",
        "\n",
        "To perform inference on a new document image, 3 things need to be done:\n",
        "\n",
        "1. prepare the image for the model using `LayoutLMv2Processor`\n",
        "2. forward pass through the model\n",
        "3. convert the output of the model to an actual label name\n",
        "\n",
        "Let's take the first image of the training dataset as an example."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "4LRSYwcgNLyc",
        "outputId": "48b07ba1-152c-4f7c-e9f0-0dab549d542e"
      },
      "source": [
        "image"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=762x1000 at 0x7F93AF24D690>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DqA8LTAE9c5u"
      },
      "source": [
        "# prepare image for the model\n",
        "encoded_inputs = processor(image, return_tensors=\"pt\")\n",
        "\n",
        "# make sure all keys of encoded_inputs are on the same device as the model\n",
        "for k,v in encoded_inputs.items():\n",
        "  encoded_inputs[k] = v.to(model.device)\n",
        "\n",
        "# forward pass\n",
        "outputs = model(**encoded_inputs)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UaiOhU8wPmVs"
      },
      "source": [
        "The model outputs `logits` of shape (batch_size, num_labels):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dC4Q5aUNPlfY",
        "outputId": "91794357-aafb-4e33-f688-9c968d14647a"
      },
      "source": [
        "logits = outputs.logits\n",
        "print(logits.shape)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([1, 15])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GIXdqx7BPqww"
      },
      "source": [
        "We simply take the largest logit (i.e. `argmax` on the last dimension), and convert it back to a string using the `id2label` dictionary we created earlier."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qS7Iz-wDPp51",
        "outputId": "dc79570b-7ed1-4b24-9075-19996a30a087"
      },
      "source": [
        "predicted_class_idx = logits.argmax(-1).item()\n",
        "print(\"Predicted class:\", id2label[predicted_class_idx])"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted class: resume\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oPraN6ybP5t6"
      },
      "source": [
        ""
      ],
      "execution_count": 27,
      "outputs": []
    }
  ]
}